chore: import upstream snapshot with attribution

examples/python/qdrant/build_collection.py

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+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+
+import base64
+
+from fastembed import TextEmbedding
+from qdrant_client import QdrantClient, models
+
+MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+
+_PRODUCTS = [
+    ("Men", "Tshirts", "Blue", "Roadster Men Blue Casual Tshirt"),
+    ("Men", "Tshirts", "Black", "HRX Men Black Sports Tshirt"),
+    ("Men", "Shoes", "White", "Nike Men White Running Sneakers"),
+    ("Men", "Shoes", "Black", "Puma Men Black Formal Shoes"),
+    ("Men", "Jeans", "Blue", "Levis Men Blue Casual Jeans"),
+    ("Women", "Tshirts", "Red", "Roadster Women Red Casual Tshirt"),
+    ("Women", "Shoes", "Red", "Steve Madden Women Red Heels"),
+    ("Women", "Shoes", "White", "Adidas Women White Running Sneakers"),
+    ("Women", "Dress", "Black", "Zara Women Black Formal Dress"),
+    ("Women", "Jeans", "Blue", "H&M Women Blue Summer Jeans"),
+]
+
+
+def build_collection(client: QdrantClient,
+                     collection: str = "fashion") -> None:
+    embedder = TextEmbedding(MODEL)
+    names = [name for _, _, _, name in _PRODUCTS]
+    vectors = [list(map(float, vector)) for vector in embedder.embed(names)]
+    if client.collection_exists(collection):
+        client.delete_collection(collection)
+    client.create_collection(
+        collection,
+        vectors_config=models.VectorParams(size=len(vectors[0]),
+                                           distance=models.Distance.COSINE),
+    )
+    points = []
+    for idx, ((gender, article, colour, name),
+              vector) in enumerate(zip(_PRODUCTS, vectors), start=1):
+        image = b"\xff\xd8\xff" + name.encode()
+        points.append(
+            models.PointStruct(
+                id=idx,
+                vector=vector,
+                payload={
+                    "gender": gender,
+                    "articleType": article,
+                    "baseColour": colour,
+                    "productDisplayName": name,
+                    "image_b64": base64.b64encode(image).decode(),
+                },
+            ))
+    client.upsert(collection, points=points)
+    for field in ("gender", "articleType", "baseColour"):
+        client.create_payload_index(
+            collection,
+            field_name=field,
+            field_schema=models.PayloadSchemaType.KEYWORD,
+        )

examples/python/qdrant/qdrant_resource.py

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+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+
+import asyncio
+import os
+
+from build_collection import MODEL, build_collection
+from qdrant_client import QdrantClient
+
+from mirage import MountMode, Workspace
+from mirage.resource.qdrant import QdrantConfig, QdrantResource
+
+
+def _client() -> QdrantClient:
+    url = os.environ.get("QDRANT_URL")
+    if url:
+        return QdrantClient(url=url, api_key=os.environ.get("QDRANT_API_KEY"))
+    return QdrantClient(host=os.environ.get("QDRANT_HOST", "localhost"),
+                        port=int(os.environ.get("QDRANT_PORT", "6333")))
+
+
+async def show(ws: Workspace, cmd: str) -> None:
+    print(f"\n=== {cmd} ===")
+    result = await ws.execute(cmd)
+    print((await result.stdout_str()).rstrip())
+
+
+async def main() -> None:
+    client = _client()
+    build_collection(client, "fashion")
+
+    config = QdrantConfig(
+        url=os.environ.get("QDRANT_URL"),
+        api_key=os.environ.get("QDRANT_API_KEY"),
+        host=os.environ.get("QDRANT_HOST", "localhost"),
+        port=int(os.environ.get("QDRANT_PORT", "6333")),
+        collection="fashion",
+        group_by=["gender", "articleType", "baseColour"],
+        id_field="id",
+        text_field="productDisplayName",
+        blob_field="image_b64",
+        blob_ext="jpg",
+        embedding_model=MODEL,
+        search_limit=4,
+    )
+    ws = Workspace({"/fashion/": QdrantResource(config)}, mode=MountMode.READ)
+
+    print("=== mounted Qdrant collection 'fashion' at /fashion/ ===")
+
+    await show(ws, "ls /fashion/")
+    await show(ws, "tree -L 2 /fashion/")
+    await show(ws, "ls /fashion/Men/Shoes/White")
+    await show(ws, "cat /fashion/Men/Shoes/White/3.txt")
+    await show(ws, "cat /fashion/Men/Shoes/White/3.json")
+
+    print("\n=== stat /fashion/Men/Shoes/White/3.jpg (raw image bytes) ===")
+    r = await ws.execute("stat -c '%s' /fashion/Men/Shoes/White/3.jpg")
+    print(f"  image size: {(await r.stdout_str()).strip()} bytes")
+
+    await show(ws, 'search "white running sneakers" /fashion')
+
+    await show(ws, "grep -ril blue /fashion/Women")
+    await show(ws, "rg -li running /fashion/Men")
+
+    print("\n=== find /fashion -name '*.txt' | wc -l ===")
+    r = await ws.execute("find /fashion -name '*.txt' | wc -l")
+    print(f"  products: {(await r.stdout_str()).strip()}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())